# 연소득당 거래금액 따른 기초 통계량
dataL2 %>%
  dplyr::summarise(
    meanVal = mean(val, na.rm = TRUE) # 평균값
    , medianVal = median(val, na.rm = TRUE) # 중앙값
    , sdVal = sd(val, na.rm = TRUE) # 표준편차
    , maxVal = max(val, na.rm = TRUE) # 최대값
    , minVal = min(val, na.rm = TRUE) # 최소값
    , cnt = n() # 개수
  ) %>%
  dplyr::arrange(desc(meanVal))

# 법정동에 따른 연소득당 거래금액 따른 기초 통계량
dataL2 %>%
  dplyr::group_by(d2) %>% 
  dplyr::summarise(
    meanVal = mean(val, na.rm = TRUE) # 평균값
    , medianVal = median(val, na.rm = TRUE) # 중앙값
    , sdVal = sd(val, na.rm = TRUE) # 표준편차
    , maxVal = max(val, na.rm = TRUE) # 최대값
    , minVal = min(val, na.rm = TRUE) # 최소값
    , cnt = n() # 개수
  ) %>%
  dplyr::arrange(desc(meanVal))

# 연소득당 거래금액 따른 히스토그램
saveImg = sprintf("%s/%s_%s.png", globalVar$figPath, serviceName, "연소득당 거래금액 따른 히스토그램")

ggplot(dataL2, aes(x = val)) +
  geom_histogram(aes(y = ..density..), colour = "black", fill = "white") +
  geom_density(alpha = 0.2) +
  geom_rug(aes(x = val, y = 0), position = position_jitter(height = 0)) +
  labs(x = "연소득당 거래금액", y = "밀도 함수", colour = NULL, fill = NULL, subtitle = "연소득당 거래금액 따른 히스토그램") +
  theme(text = element_text(size = 16)) # +

  # ggsave(filename = saveImg, width = 12, height = 6, dpi = 600)

# 법정동에 따른 연소득당 거래금액 히스토그램
saveImg = sprintf("%s/%s_%s.png", globalVar$figPath, serviceName, "법정동에 따른 연소득당 거래금액 히스토그램")

ggplot(dataL3, aes(x = d2, y = meanVal, fill = meanVal)) +
  geom_bar(position = "dodge", stat = "identity") +
  geom_text(aes(label = round(meanVal, 0)), vjust = 1.6, color = "white", size = 4) +
  labs(x = "법정동", y = "연소득당 거래금액", fill = NULL, subtitle = "법정동에 따른 연소득당 거래금액 히스토그램") +
  scale_fill_gradientn(colours = cbMatlab, na.value = NA) +
  theme(
    text = element_text(size = 16)
    , axis.text.x = element_text(angle = 45, hjust = 1)
  ) # +

  # ggsave(filename = saveImg, width = 12, height = 8, dpi = 600)

# 연소득당 거래금액 따른 상자 그림
saveImg = sprintf("%s/%s_%s.png", globalVar$figPath, serviceName, "연소득당 거래금액 따른 상자 그림")

ggplot(dataL2, aes(y = val)) +
  geom_boxplot() +
  labs(x = NULL, y = "연소득당 거래금액", colour = NULL, fill = NULL, subtitle = "연소득당 거래금액 따른 상자 그림") +
  theme(text = element_text(size = 16)) # +

  # ggsave(filename = saveImg, width = 12, height = 6, dpi = 600)

# 법정동에 따른 연소득당 거래금액 상자 그림
saveImg = sprintf("%s/%s_%s.png", globalVar$figPath, serviceName, "법정동에 따른 연소득당 거래금액 상자 그림")

ggplot(dataL2, aes(x = d2, y = val, color = d2)) +
  geom_boxplot() +
  labs(x = "법정동", y = "연소득당 거래금액", color = "법정동", fill = NULL, subtitle = "법정동에 따른 연소득당 거래금액 상자 그림") +
  # scale_colour_gradientn(colours = cbMatlab, na.value = NA) +
  theme(
    text = element_text(size = 16)
    , axis.text.x = element_text(angle = 45, hjust = 1)
  ) # +

  # ggsave(filename = saveImg, width = 12, height = 8, dpi = 600)

saveImg = sprintf("%s/%s_%s.png", globalVar$figPath, serviceName, "연소득당 거래금액 산점도")

ggpubr::ggscatter(
  dataL2, x = "meanCost", y = "거래금액"
  , add = "reg.line", conf.int = TRUE, scales = "free_x"
  # , facet.by = "전체 법정동"
  , add.params = list(color = "blue", fill = "lightblue")
) +
  labs(
    title = NULL
    , x = "연소득"
    , y = "거래금액"
    , color = NULL
    , subtitle = "연소득당 거래금액 산점도"
  ) +
  theme_bw() +
  ggpubr::stat_regline_equation(label.x.npc = 0.0, label.y.npc = 1.0, size = 5) +
  ggpubr::stat_cor(label.x.npc = 0.0, label.y.npc = 0.90, p.accuracy  =  0.01,  r.accuracy  =  0.01, size = 5) +
  theme(text = element_text(size = 16)) # +

  # ggsave(filename = saveImg, width = 8, height = 8, dpi = 600)

addrList = dataL2$addr %>% unique() %>% sort() %>%
  as.tibble()

# 구글 API 하루 제한
# addrData = ggmap::mutate_geocode(addrList, value, source = "google")

# 각 주소에 따라 위/경도 반환
# for (i in 1:nrow(addrList)) {
#   addrData = ggmap::mutate_geocode(addrList[i, 'value'], value, source = "google")
# 
#   if (nrow(addrData) < 1) { next }
# 
#   readr::write_csv(x = addrData, file = saveFile, append = TRUE)
# }

saveFile = sprintf("%s/%s_%s.csv", globalVar$inpPath, serviceName, "seoul apartment transaction-addrData")
addrData =  readr::read_csv(file = saveFile, col_names = c("value", "lon", "lat"))

dataL4 = dataL2 %>% 
  dplyr::left_join(addrData, by = c("addr" = "value")) %>% 
  dplyr::filter(
    ! is.na(lon)
    , ! is.na(lat)
    , dplyr::between(lon, 120, 130)
    , dplyr::between(lat, 30, 40)
  ) %>% 
  dplyr::group_by(lon, lat, addr) %>% 
  dplyr::summarise(
    meanVal = mean(val, na.rm = TRUE)
  )


map = ggmap::get_map(
  location = c(lon = mean(dataL4$lon, na.rm = TRUE), lat = mean(dataL4$lat, na.rm = TRUE))
  , zoom = 12
  , maptype = "hybrid"
)

saveImg = sprintf("%s/%s_%s.png", globalVar$figPath, serviceName, "연소득당 거래금액 지도 매핑")

ggmap(map, extent = "device") +
  geom_point(data = dataL4, aes(x = lon, y = lat, color = meanVal, size = meanVal, alpha = 0.3)) +
  scale_color_gradientn(colours = cbMatlab, na.value = NA) +
  labs(
    subtitle = NULL
    , x = NULL
    , y = NULL
    , fill = NULL
    , colour = NULL
    , title = NULL
    , size = NULL
  ) +
  scale_alpha(guide = 'none') +
  theme(
    text = element_text(size = 18)
  ) # +

  # ggsave(filename = saveImg, width = 10, height = 10, dpi = 600)

#***********************************************
# IDW 지도 그리기
#***********************************************
dtYearList = dataL2$dtYear %>% unique() %>% sort()

# dtYearInfo = 2017
for (dtYearInfo in dtYearList) {
  
  dataL5 = dataL2 %>% 
    dplyr::left_join(addrData, by = c("addr" = "value")) %>% 
    dplyr::filter(
      ! is.na(lon)
      , ! is.na(lat)
      , dplyr::between(lon, 120, 130)
      , dplyr::between(lat, 30, 40)
      , dtYear == dtYearInfo
    ) %>% 
    dplyr::group_by(lon, lat, addr) %>% 
    dplyr::summarise(
      meanVal = mean(val, na.rm = TRUE)
    )
  
  # 면적당 거래금액 지도 집중도
  spNewData = expand.grid(
    x = seq(from = min(dataL5$lon, na.rm = TRUE), to = max(dataL5$lon, na.rm = TRUE), by = 0.003)
    , y = seq(from = min(dataL5$lat, na.rm = TRUE), to = max(dataL5$lat, na.rm = TRUE), by = 0.003)
  )
  sp::coordinates(spNewData) = ~ x + y
  sp::gridded(spNewData) = TRUE
  
  spData = dataL5
  sp::coordinates(spData) = ~ lon + lat
  
  # IDW 학습 및 전처리수행
  spDataL1 = gstat::idw(
    formula = meanVal ~ 1
    , locations = spData
    , newdata = spNewData
    , nmax = 4
  ) %>%
    as.data.frame() %>%
    dplyr::rename(
      lon = x
      , lat = y
      , val = var1.pred
    ) %>%
    dplyr::select(-var1.var) %>% 
    as.tibble()
  
  summary(spDataL1)
  saveImg = sprintf("%s/%s_%s.png", globalVar$figPath, serviceName, stringr::str_c(dtYearInfo, "년 연소득당 거래금액 IDW 지도 매핑"))
  
resPlot = ggmap(map, extent = "device") +
    geom_tile(data = spDataL1, aes(x = lon, y = lat, fill = val, alpha = 0.2)) +
    # geom_raster(data = spDataL1, aes(x = lon, y = lat, fill = val, alpha = 0.2)) +
    # scale_color_gradientn(colours = cbMatlab, na.value = NA) +
    scale_fill_gradientn(colours = cbMatlab, na.value = NA) +
    labs(
      subtitle = stringr::str_c(dtYearInfo, "년 연소득당 거래금액 IDW 지도 매핑")
      , x = NULL
      , y = NULL
      , fill = NULL
      , colour = NULL
      , title = NULL
      , size = NULL
    ) +
    scale_alpha(guide = 'none') +
    theme(
      text = element_text(size = 16)
    ) # +
    # ggsave(filename = saveImg, width = 10, height = 10, dpi = 600)

    print(resPlot)
}

#==========================================
# TMAP 주제도 그리기
#==========================================
mapInfo = Sys.glob(paste(globalVar$mapPath, "/admCode/TL_SCCO_SIG.shp", sep = "/"))
mapShape = sf::st_read(mapInfo, options = "ENCODING=EUC-KR")

# 전체 법정동에 따른 연소득당 거래금액 주제도
dataL6 = dataL2 %>% 
  dplyr::left_join(addrData, by = c("addr" = "value")) %>% 
  dplyr::filter(
    ! is.na(lon)
    , ! is.na(lat)
    , dplyr::between(lon, 120, 130)
    , dplyr::between(lat, 30, 40)
  ) %>% 
  dplyr::group_by(emdCd) %>%
  dplyr::summarise(
    meanVal = mean(val, na.rm = TRUE)
  )

mapShapeL1 = mapShape %>% 
  dplyr::inner_join(dataL6, by = c("SIG_CD" = "emdCd"))

setTilte = "전체 법정동에 따른 연소득당 거래금액 주제도"
# saveImg = sprintf("%s/%s_%s.png", globalVar$figPath, serviceName,  setTilte)

tmap::tmap_mode("view")

tmap::tm_shape(mapShapeL1) +
  tmap::tm_polygons(col = "meanVal", alpha = 0.5, palette = cbMatlab, legend.hist = TRUE, style = "cont") +
  tmap::tm_text(text = "SIG_KOR_NM") +
  tmap::tm_legend(outside = TRUE) + 
  tmap::tm_layout(title = setTilte)

  # tmap_save(filename = saveImg, width = 10, height = 10, dpi = 600)

# 연도별 법정동에 따른 연소득당 거래금액 주제도

# dtYearInfo = 2017
for (i in 1:length(dtYearList)) {

  dataL7 = dataL2 %>% 
    dplyr::left_join(addrData, by = c("addr" = "value")) %>% 
    dplyr::filter(
      ! is.na(lon)
      , ! is.na(lat)
      , dplyr::between(lon, 120, 130)
      , dplyr::between(lat, 30, 40)
      , dtYear == dtYearList[i]
    ) %>% 
    dplyr::group_by(emdCd) %>%
    dplyr::summarise(
      meanVal = mean(val, na.rm = TRUE)
    )
  
  mapShapeL1 = mapShape %>% 
    dplyr::inner_join(dataL7, by = c("SIG_CD" = "emdCd"))

  setTilte = stringr::str_c(dtYearList[i], "년 법정동에 따른 연소득당 거래금액 주제도")
  # saveImg = sprintf("%s/%s_%s.png", globalVar$figPath, serviceName, setTilte)
  
  tmap::tmap_mode("view")
  
  resPlot = tmap::tm_shape(mapShapeL1) +
    tmap::tm_polygons(col = "meanVal", alpha = 0.5, palette = cbMatlab, legend.hist = TRUE, style = "cont") +
    tmap::tm_text(text = "SIG_KOR_NM") +
    tmap::tm_legend(outside = TRUE) + 
    tmap::tm_layout(title = setTilte)
    # tmap_save(filename = saveImg, width = 10, height = 10, dpi = 600)

  print(resPlot)  
}